1. Field of the Invention
The present invention generally relates to generating recommendations. More particularly, the present invention relates to using several filtering processes to generate recommendations.
2. Related Art
In fields such as E-commerce, a recommendation system applies data analysis techniques to provide a list of top-N recommended items for a given customer. Many of these recommendation systems are based on any of the following filtering processes: collaborative filtering (CF), information filtering (IF), and association rule based filtering (RF).
Collaborative Filtering (CF) is used to identify a set of other customers with preferences similar to a given customer, known as his neighbors, and to generate a recommendation based on their opinions.
Information Filtering (IF) is used to generate recommendations solely based on the profiles expressing the given customer's needs or preferences. These profiles are built in terms of analyzing the content of items that the customer has rated, or summarizing the customer's shopping behavior. Each customer is dealt with individually.
Association Rule based Filtering (RF) uses the correlation between items to make predictions. For example, when TV->VCR is a strong cross-sale association rule (with confidence and support over certain thresholds), if a customer bought a TV, meaning that he supports this rule, then he will be recommended to buy a VCR.
A number of previous experimental studies show that a recommendation system based on CF, IF or RF alone has considerable limitations in accuracy and scalability.
A pure (or “flat”) CF based recommendation system can be ineffective for several reasons. For example, prediction for the items with too few ratings cannot be provided (early rater problem); the similarity between many customers is low (sparsity problem); provide recommendations over all the customers and products is poorly focused; the causal relationships of customers' shopping activities (e.g., association rules) are missed.
A pure (or “flat”) IF based recommendation system is less affected by the limitations of CF based recommendation systems since it is only sensitive to the items themselves rather than the correlation between customers. However, a pure IF based recommendation system is restricted to referring to items similar to those already rated or bought (over specification problem), and cannot perform well when lacking of content information and cross-reference.
A pure (or “flat”) RF based recommendation system is also ineffective since many rules may not be relevant to the given customer. Moreover, selecting rules based on a fixed minimal support and confidence thresholds may lead to either too many or too few rules. Also, mining rules based on a large amount of customers involving a large set of products is very expensive.
Examples of the limitations of these “flat” recommendation systems will now be described. In the winter season, a man may be recommended with a woman's cloth for summer, if his shopping history in purchasing other products (e.g., office supply, electronics, etc.) is similar to some women. As another example, without limiting the prediction base, generating association rules in real-time for providing recommendations is almost impossible.